Spatio-temporal Crop Classification On Volumetric Data

TL;DR

This paper introduces a novel 3D and 1D CNN architecture for large-area crop classification that outperforms existing methods in accuracy, efficiency, and inference speed on benchmark datasets.

Contribution

The paper presents a new combined spatio-temporal CNN approach that improves crop classification accuracy over classical and recent deep learning methods.

Findings

Achieved 2% higher classification accuracy than existing methods.

Maintained minimal model parameters and low inference time.

Validated on Yolo and Imperial county datasets.

Abstract

Large-area crop classification using multi-spectral imagery is a widely studied problem for several decades and is generally addressed using classical Random Forest classifier. Recently, deep convolutional neural networks (DCNN) have been proposed. However, these methods only achieved results comparable with Random Forest. In this work, we present a novel CNN based architecture for large-area crop classification. Our methodology combines both spatio-temporal analysis via 3D CNN as well as temporal analysis via 1D CNN. We evaluated the efficacy of our approach on Yolo and Imperial county benchmark datasets. Our combined strategy outperforms both classical as well as recent DCNN based methods in terms of classification accuracy by 2% while maintaining a minimum number of parameters and the lowest inference time.